US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Write and test product or system development code.
Develop representation learning models, including embedding or encoder models trained with contrastive, supervised, or self-supervised (e.g., masked-prediction) objectives, and set or sequence encoders (attention pooling, Transformers).
Execute clustering or metric learning at scale (e.g., hierarchical/agglomerative, density-based), and assess cluster quality.
Utilize Large Language Model (LLMs) in ML systems (e.g., LLM embeddings, autoraters for evaluation) and distill LLM judgments into classifiers.
Minimum qualifications
Bachelor’s degree or equivalent practical experience.
5 years of experience building and launching production ML models, including 2 years of experience with recommendation, retrieval, search, or personalization systems.
1 year of experience in the design, analysis, and optimization of algorithms for high-performance systems, and developing and implementing Machine Learning algorithms (e.g., classification, regression, clustering, deep learning) for practical applications.
Experience with large-scale data processing (Beam, Spark, or similar), in Python, C++, and SQL, and with JAX, TensorFlow, or PyTorch.
Preferred qualifications
Master's degree or PhD in Computer Science or related technical field.
5 years of experience with data structures and algorithms.
1 year of experience in a technical leadership role.
Experience developing accessible technologies.
A proven track record driving quality projects end to end, from design through A/B experimentation to launch.
Bachelor’s degree or equivalent practical experience.
5 years of experience building and launching production ML models, including 2 years of experience with recommendation, retrieval, search, or personalization systems.
1 year of experience in the design, analysis, and optimization of algorithms for high-performance systems, and developing and implementing Machine Learning algorithms (e.g., classification, regression, clustering, deep learning) for practical applications.
Experience with large-scale data processing (Beam, Spark, or similar), in Python, C++, and SQL, and with JAX, TensorFlow, or PyTorch.
